Attitudes Regarding the Assignment Process Among Navy Medical Corps Officers
Date: 2018-06-30
This report covers the survey about attitudes collected by Richard Childers, MD and Joel Schofer, MD.
1 Summary
1.1 Notes
- The current report covers 951 responses.
- We excluded 17 cases because their orders preceded the year 2012 and 26 cases because the
year_executed_ordervalue was missing.
1.2 Unanswered Questions
1.3 Answered Questions
2 Graphs
Marginals
2.1 Scatterplots
3 Relationships between Outcomes
| satistfaction_rank | transparency_rank | favoritism_rank | assignment_current_choice | |
|---|---|---|---|---|
| satistfaction_rank | 1.0000000 | 0.7711209 | 0.4859620 | -0.5189457 |
| transparency_rank | 0.7711209 | 1.0000000 | 0.4877154 | -0.4053583 |
| favoritism_rank | 0.4859620 | 0.4877154 | 1.0000000 | -0.3245715 |
| assignment_current_choice | -0.5189457 | -0.4053583 | -0.3245715 | 1.0000000 |
4 Analyses - 1 Predictor
4.1 By Rank
### satistfaction_rank
Call:
lm(formula = satistfaction_rank ~ 1 + officer_rate_f, data = ds)
Residuals:
Min 1Q Median 3Q Max
-3.1296 -0.9315 0.4091 1.0685 1.8639
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.13615 0.09033 34.720 < 2e-16
officer_rate_f4 0.45476 0.11587 3.925 9.37e-05
officer_rate_f5 0.79536 0.12686 6.269 5.71e-10
officer_rate_f6 0.99348 0.15572 6.380 2.89e-10
Residual standard error: 1.318 on 866 degrees of freedom
(81 observations deleted due to missingness)
Multiple R-squared: 0.06191, Adjusted R-squared: 0.05866
F-statistic: 19.05 on 3 and 866 DF, p-value: 5.713e-12### transparency_rank
Call:
lm(formula = transparency_rank ~ 1 + officer_rate_f, data = ds)
Residuals:
Min 1Q Median 3Q Max
-2.9444 -0.9019 0.2009 1.2009 2.0981
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.9019 0.0924 31.407 < 2e-16
officer_rate_f4 0.5921 0.1183 5.003 6.82e-07
officer_rate_f5 0.8972 0.1299 6.906 9.61e-12
officer_rate_f6 1.0426 0.1595 6.535 1.08e-10
Residual standard error: 1.352 on 871 degrees of freedom
(76 observations deleted due to missingness)
Multiple R-squared: 0.06859, Adjusted R-squared: 0.06538
F-statistic: 21.38 on 3 and 871 DF, p-value: 2.278e-13### favoritism_rank
Call:
lm(formula = favoritism_rank ~ 1 + officer_rate_f, data = ds)
Residuals:
Min 1Q Median 3Q Max
-2.4190 -1.1216 -0.1216 1.2297 1.8784
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.1216 0.1092 28.575 <2e-16
officer_rate_f4 0.2029 0.1334 1.521 0.1286
officer_rate_f5 0.2582 0.1429 1.807 0.0712
officer_rate_f6 0.2974 0.1696 1.754 0.0798
Residual standard error: 1.329 on 759 degrees of freedom
(188 observations deleted due to missingness)
Multiple R-squared: 0.005615, Adjusted R-squared: 0.001685
F-statistic: 1.429 on 3 and 759 DF, p-value: 0.2331### assignment_current_choice
Call:
lm(formula = assignment_current_choice ~ 1 + officer_rate_f,
data = ds)
Residuals:
Min 1Q Median 3Q Max
-0.8177 -0.6507 -0.4040 0.3493 3.6080
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.81771 0.08084 22.484 < 2e-16
officer_rate_f4 -0.16702 0.10408 -1.605 0.108957
officer_rate_f5 -0.42575 0.11332 -3.757 0.000185
officer_rate_f6 -0.41367 0.13860 -2.985 0.002929
Residual standard error: 1.12 on 778 degrees of freedom
(169 observations deleted due to missingness)
Multiple R-squared: 0.02228, Adjusted R-squared: 0.01851
F-statistic: 5.908 on 3 and 778 DF, p-value: 0.00054784.2 By Specialty Type
### satistfaction_rank
Call:
lm(formula = satistfaction_rank ~ 1 + specialty_type, data = ds[ds$specialty_type !=
"unknown", ])
Residuals:
Min 1Q Median 3Q Max
-2.91724 -0.91724 0.08276 1.08276 2.33721
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.91724 0.06257 62.603 < 2e-16
specialty_typesurgical -0.23268 0.12388 -1.878 0.0607
specialty_typefamily -0.56358 0.11959 -4.713 2.85e-06
specialty_typeoperational -1.25445 0.15401 -8.145 1.32e-15
specialty_typeresident -0.40209 0.23564 -1.706 0.0883
Residual standard error: 1.305 on 862 degrees of freedom
(78 observations deleted due to missingness)
Multiple R-squared: 0.0812, Adjusted R-squared: 0.07693
F-statistic: 19.04 on 4 and 862 DF, p-value: 5.069e-15### transparency_rank
Call:
lm(formula = transparency_rank ~ 1 + specialty_type, data = ds[ds$specialty_type !=
"unknown", ])
Residuals:
Min 1Q Median 3Q Max
-2.7215 -0.7215 0.2785 1.2785 2.3837
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.72146 0.06464 57.574 < 2e-16
specialty_typesurgical -0.08881 0.12895 -0.689 0.4912
specialty_typefamily -0.48510 0.12357 -3.926 9.33e-05
specialty_typeoperational -1.10518 0.15955 -6.927 8.39e-12
specialty_typeresident -0.58257 0.23454 -2.484 0.0132
Residual standard error: 1.353 on 867 degrees of freedom
(73 observations deleted due to missingness)
Multiple R-squared: 0.06323, Adjusted R-squared: 0.05891
F-statistic: 14.63 on 4 and 867 DF, p-value: 1.431e-11### favoritism_rank
Call:
lm(formula = favoritism_rank ~ 1 + specialty_type, data = ds[ds$specialty_type !=
"unknown", ])
Residuals:
Min 1Q Median 3Q Max
-2.57143 -1.08029 -0.08029 1.29412 2.29412
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.46923 0.06627 52.349 < 2e-16
specialty_typesurgical -0.13590 0.12963 -1.048 0.29481
specialty_typefamily -0.38894 0.12998 -2.992 0.00286
specialty_typeoperational -0.76335 0.17199 -4.438 1.04e-05
specialty_typeresident 0.10220 0.25605 0.399 0.68991
Residual standard error: 1.309 on 756 degrees of freedom
(184 observations deleted due to missingness)
Multiple R-squared: 0.03281, Adjusted R-squared: 0.02769
F-statistic: 6.411 on 4 and 756 DF, p-value: 4.477e-05### assignment_current_choice
Call:
lm(formula = assignment_current_choice ~ 1 + specialty_type,
data = ds[ds$specialty_type != "unknown", ])
Residuals:
Min 1Q Median 3Q Max
-0.9846 -0.5870 -0.4912 0.4130 3.5088
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.49123 0.05612 26.572 < 2e-16
specialty_typesurgical 0.33611 0.11041 3.044 0.00241
specialty_typefamily 0.09573 0.11071 0.865 0.38747
specialty_typeoperational 0.49339 0.14994 3.291 0.00105
specialty_typeresident -0.20918 0.18807 -1.112 0.26639
Residual standard error: 1.121 on 775 degrees of freedom
(165 observations deleted due to missingness)
Multiple R-squared: 0.0255, Adjusted R-squared: 0.02047
F-statistic: 5.07 on 4 and 775 DF, p-value: 0.00048944.3 By Bonus Pay
### satistfaction_rank
Call:
lm(formula = satistfaction_rank ~ 1 + bonus_pay_cut4, data = ds)
Residuals:
Min 1Q Median 3Q Max
-2.7701 -0.7521 0.2743 1.2479 2.1589
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.8411 0.1286 22.098 < 2e-16
bonus_pay_cut4$20-24k 0.8846 0.1423 6.215 7.98e-10
bonus_pay_cut4$24-32k 0.9290 0.1634 5.686 1.78e-08
bonus_pay_cut432k+ 0.9110 0.1779 5.121 3.74e-07
Residual standard error: 1.33 on 868 degrees of freedom
(79 observations deleted due to missingness)
Multiple R-squared: 0.04722, Adjusted R-squared: 0.04393
F-statistic: 14.34 on 3 and 868 DF, p-value: 3.984e-09### transparency_rank
Call:
lm(formula = transparency_rank ~ 1 + bonus_pay_cut4, data = ds)
Residuals:
Min 1Q Median 3Q Max
-2.7688 -0.7688 0.4483 1.2312 2.3636
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.6364 0.1297 20.328 < 2e-16
bonus_pay_cut4$20-24k 0.9138 0.1438 6.353 3.39e-10
bonus_pay_cut4$24-32k 1.1324 0.1659 6.827 1.62e-11
bonus_pay_cut432k+ 0.9154 0.1810 5.057 5.20e-07
Residual standard error: 1.36 on 873 degrees of freedom
(74 observations deleted due to missingness)
Multiple R-squared: 0.05592, Adjusted R-squared: 0.05267
F-statistic: 17.24 on 3 and 873 DF, p-value: 7.02e-11### favoritism_rank
Call:
lm(formula = favoritism_rank ~ 1 + bonus_pay_cut4, data = ds)
Residuals:
Min 1Q Median 3Q Max
-2.4231 -1.0119 -0.2609 0.9881 1.9881
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.0119 0.1449 20.792 <2e-16
bonus_pay_cut4$20-24k 0.3296 0.1590 2.073 0.0385
bonus_pay_cut4$24-32k 0.4112 0.1797 2.288 0.0224
bonus_pay_cut432k+ 0.2490 0.1906 1.306 0.1918
Residual standard error: 1.328 on 761 degrees of freedom
(186 observations deleted due to missingness)
Multiple R-squared: 0.007504, Adjusted R-squared: 0.003592
F-statistic: 1.918 on 3 and 761 DF, p-value: 0.1252### assignment_current_choice
Call:
lm(formula = assignment_current_choice ~ 1 + bonus_pay_cut4,
data = ds)
Residuals:
Min 1Q Median 3Q Max
-0.7979 -0.6239 -0.5190 0.3761 3.4810
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.7979 0.1164 15.446 <2e-16
bonus_pay_cut4$20-24k -0.2789 0.1287 -2.167 0.0305
bonus_pay_cut4$24-32k -0.1312 0.1468 -0.894 0.3718
bonus_pay_cut432k+ -0.1740 0.1589 -1.095 0.2736
Residual standard error: 1.129 on 780 degrees of freedom
(167 observations deleted due to missingness)
Multiple R-squared: 0.007209, Adjusted R-squared: 0.00339
F-statistic: 1.888 on 3 and 780 DF, p-value: 0.13014.4 By Assignment Current Choice
### satistfaction_rank
Call:
lm(formula = satistfaction_rank ~ 1 + assignment_current_choice,
data = ds)
Residuals:
Min 1Q Median 3Q Max
-3.1471 -0.8584 0.1416 0.8529 3.1416
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.71924 0.06742 69.99 <2e-16
assignment_current_choice -0.57218 0.03421 -16.73 <2e-16
Residual standard error: 1.075 on 759 degrees of freedom
(190 observations deleted due to missingness)
Multiple R-squared: 0.2693, Adjusted R-squared: 0.2683
F-statistic: 279.7 on 1 and 759 DF, p-value: < 2.2e-16### transparency_rank
Call:
lm(formula = transparency_rank ~ 1 + assignment_current_choice,
data = ds)
Residuals:
Min 1Q Median 3Q Max
-2.92074 -0.92074 0.07926 1.07926 2.97447
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.39455 0.07585 57.94 <2e-16
assignment_current_choice -0.47380 0.03873 -12.23 <2e-16
Residual standard error: 1.21 on 761 degrees of freedom
(188 observations deleted due to missingness)
Multiple R-squared: 0.1643, Adjusted R-squared: 0.1632
F-statistic: 149.6 on 1 and 761 DF, p-value: < 2.2e-16### favoritism_rank
Call:
lm(formula = favoritism_rank ~ 1 + assignment_current_choice,
data = ds)
Residuals:
Min 1Q Median 3Q Max
-2.6155 -0.6155 0.1258 1.3845 2.8670
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.98608 0.08223 48.477 <2e-16
assignment_current_choice -0.37062 0.04163 -8.902 <2e-16
Residual standard error: 1.232 on 673 degrees of freedom
(276 observations deleted due to missingness)
Multiple R-squared: 0.1053, Adjusted R-squared: 0.104
F-statistic: 79.25 on 1 and 673 DF, p-value: < 2.2e-164.5 By Year
### satistfaction_rank
4.6 By Survey Lag
### satistfaction_rank
4.7 By Manning Proportion
### manning_proportion
4.8 By Crtical War
4.9 By Billet Current
Call:
lm(formula = satistfaction_rank ~ 1 + billet_current, data = ds)
Residuals:
Min 1Q Median 3Q Max
-3.0000 -0.8565 0.1435 1.1435 2.5500
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.85651 0.06173 62.473 < 2e-16
billet_currentGME -0.04742 0.13966 -0.340 0.73427
billet_currentNon-Operational/Non-Clinical -0.21068 0.19944 -1.056 0.29109
billet_currentOCONUS MTF -0.44651 0.14517 -3.076 0.00217
billet_currentCONUS Operational -0.74044 0.13865 -5.340 1.19e-07
billet_currentOCONUS Operational -1.40651 0.21672 -6.490 1.44e-10
billet_currentOther 0.14349 0.44229 0.324 0.74570
Residual standard error: 1.314 on 865 degrees of freedom
(79 observations deleted due to missingness)
Multiple R-squared: 0.07331, Adjusted R-squared: 0.06688
F-statistic: 11.41 on 6 and 865 DF, p-value: 2.681e-125 Analyses - 2 Predictors
5.1 By Rank and Specialty Type
### satistfaction_rank
Call:
lm(formula = satistfaction_rank ~ 1 + officer_rate_f * specialty_type,
data = ds[ds$specialty_type != "unknown", ])
Residuals:
Min 1Q Median 3Q Max
-3.3333 -0.7794 0.2299 1.0104 2.5873
Coefficients: (2 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.779412 0.155378 24.324 < 2e-16
officer_rate_f4 -0.009297 0.183241 -0.051 0.95955
officer_rate_f5 0.256049 0.189171 1.354 0.17624
officer_rate_f6 0.553922 0.237344 2.334 0.01984
specialty_typesurgical -0.112745 0.365497 -0.308 0.75780
specialty_typefamily -0.931586 0.244604 -3.809 0.00015
specialty_typeoperational -1.366713 0.224055 -6.100 1.61e-09
specialty_typeresident -0.229412 0.325924 -0.704 0.48170
officer_rate_f4:specialty_typesurgical -0.238451 0.406457 -0.587 0.55759
officer_rate_f5:specialty_typesurgical 0.051643 0.432811 0.119 0.90505
officer_rate_f6:specialty_typesurgical -0.125350 0.493916 -0.254 0.79972
officer_rate_f4:specialty_typefamily 0.624434 0.315702 1.978 0.04826
officer_rate_f5:specialty_typefamily 0.328557 0.340359 0.965 0.33466
officer_rate_f6:specialty_typefamily 0.487141 0.390927 1.246 0.21307
officer_rate_f4:specialty_typeoperational 0.668027 0.420593 1.588 0.11259
officer_rate_f5:specialty_typeoperational 2.331252 1.305191 1.786 0.07443
officer_rate_f6:specialty_typeoperational 0.658380 0.536284 1.228 0.21991
officer_rate_f4:specialty_typeresident -0.079165 0.491879 -0.161 0.87218
officer_rate_f5:specialty_typeresident NA NA NA NA
officer_rate_f6:specialty_typeresident NA NA NA NA
Residual standard error: 1.281 on 848 degrees of freedom
(79 observations deleted due to missingness)
Multiple R-squared: 0.125, Adjusted R-squared: 0.1074
F-statistic: 7.124 on 17 and 848 DF, p-value: < 2.2e-16
Call:
lm(formula = satistfaction_rank ~ 1 + officer_rate_f + specialty_type,
data = ds[ds$specialty_type != "unknown", ])
Residuals:
Min 1Q Median 3Q Max
-3.3867 -0.7954 0.2046 0.9416 2.4460
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.6060 0.1139 31.673 < 2e-16
officer_rate_f4 0.1894 0.1227 1.543 0.123167
officer_rate_f5 0.4625 0.1364 3.390 0.000731
officer_rate_f6 0.7807 0.1587 4.918 1.05e-06
specialty_typesurgical -0.2466 0.1222 -2.018 0.043938
specialty_typefamily -0.5476 0.1188 -4.608 4.67e-06
specialty_typeoperational -1.0521 0.1657 -6.351 3.47e-10
specialty_typeresident -0.1655 0.2383 -0.695 0.487552
Residual standard error: 1.282 on 858 degrees of freedom
(79 observations deleted due to missingness)
Multiple R-squared: 0.1131, Adjusted R-squared: 0.1058
F-statistic: 15.62 on 7 and 858 DF, p-value: < 2.2e-16TODO: examine if the interaction term significantly improves fit.### transparency_rank
Call:
lm(formula = transparency_rank ~ 1 + specialty_type, data = ds)
Residuals:
Min 1Q Median 3Q Max
-2.7215 -0.7215 0.2785 1.2785 2.3837
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.72146 0.06481 57.418 < 2e-16
specialty_typesurgical -0.08881 0.12930 -0.687 0.4924
specialty_typefamily -0.48510 0.12390 -3.915 9.74e-05
specialty_typeoperational -1.10518 0.15999 -6.908 9.48e-12
specialty_typeresident -0.58257 0.23518 -2.477 0.0134
specialty_typeunknown -0.72146 0.61008 -1.183 0.2373
Residual standard error: 1.356 on 871 degrees of freedom
(74 observations deleted due to missingness)
Multiple R-squared: 0.06327, Adjusted R-squared: 0.0579
F-statistic: 11.77 on 5 and 871 DF, p-value: 4.985e-11### favoritism_rank
Call:
lm(formula = favoritism_rank ~ 1 + specialty_type, data = ds)
Residuals:
Min 1Q Median 3Q Max
-2.57143 -1.08029 -0.08029 1.29412 2.29412
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.46923 0.06646 52.197 < 2e-16
specialty_typesurgical -0.13590 0.13001 -1.045 0.29621
specialty_typefamily -0.38894 0.13036 -2.984 0.00294
specialty_typeoperational -0.76335 0.17249 -4.425 1.1e-05
specialty_typeresident 0.10220 0.25680 0.398 0.69077
specialty_typeunknown -0.21923 0.65964 -0.332 0.73972
Residual standard error: 1.313 on 759 degrees of freedom
(186 observations deleted due to missingness)
Multiple R-squared: 0.03251, Adjusted R-squared: 0.02613
F-statistic: 5.101 on 5 and 759 DF, p-value: 0.0001313### assignment_current_choice
Call:
lm(formula = assignment_current_choice ~ 1 + specialty_type,
data = ds)
Residuals:
Min 1Q Median 3Q Max
-0.9846 -0.5870 -0.4912 0.4130 3.5088
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.49123 0.05603 26.613 < 2e-16
specialty_typesurgical 0.33611 0.11024 3.049 0.00237
specialty_typefamily 0.09573 0.11053 0.866 0.38673
specialty_typeoperational 0.49339 0.14971 3.296 0.00103
specialty_typeresident -0.20918 0.18778 -1.114 0.26565
specialty_typeunknown -0.24123 0.56244 -0.429 0.66812
Residual standard error: 1.119 on 778 degrees of freedom
(167 observations deleted due to missingness)
Multiple R-squared: 0.02595, Adjusted R-squared: 0.01969
F-statistic: 4.146 on 5 and 778 DF, p-value: 0.0010085.2 By Rank and Assignment Current Choice
### satistfaction_rank
Call:
lm(formula = satistfaction_rank ~ 1 + officer_rate_f + assignment_current_choice,
data = ds)
Residuals:
Min 1Q Median 3Q Max
-3.5109 -0.6317 0.2528 0.7550 3.4083
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.28605 0.09967 43.00 < 2e-16
officer_rate_f4 0.39009 0.09978 3.91 0.000101
officer_rate_f5 0.53451 0.10886 4.91 1.12e-06
officer_rate_f6 0.76370 0.13259 5.76 1.22e-08
assignment_current_choice -0.53887 0.03382 -15.94 < 2e-16
Residual standard error: 1.048 on 754 degrees of freedom
(192 observations deleted due to missingness)
Multiple R-squared: 0.3052, Adjusted R-squared: 0.3015
F-statistic: 82.8 on 4 and 754 DF, p-value: < 2.2e-16
Call:
lm(formula = satistfaction_rank ~ 1 + officer_rate_f * assignment_current_choice,
data = ds)
Residuals:
Min 1Q Median 3Q Max
-3.4981 -0.6452 0.2672 0.7416 3.3548
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.25471 0.13343 31.887 < 2e-16
officer_rate_f4 0.41589 0.17248 2.411 0.016137
officer_rate_f5 0.65201 0.19187 3.398 0.000714
officer_rate_f6 0.75118 0.22544 3.332 0.000904
assignment_current_choice -0.52190 0.05871 -8.889 < 2e-16
officer_rate_f4:assignment_current_choice -0.01366 0.07963 -0.171 0.863895
officer_rate_f5:assignment_current_choice -0.07892 0.10189 -0.775 0.438854
officer_rate_f6:assignment_current_choice 0.01408 0.11960 0.118 0.906346
Residual standard error: 1.05 on 751 degrees of freedom
(192 observations deleted due to missingness)
Multiple R-squared: 0.3059, Adjusted R-squared: 0.2994
F-statistic: 47.28 on 7 and 751 DF, p-value: < 2.2e-16Analysis of Variance Table
Model 1: satistfaction_rank ~ 1 + officer_rate_f + assignment_current_choice
Model 2: satistfaction_rank ~ 1 + officer_rate_f * assignment_current_choice
Res.Df RSS Df Sum of Sq F Pr(>F)
1 754 828.44
2 751 827.64 3 0.80396 0.2432 0.8662### transparency_rank
Call:
lm(formula = transparency_rank ~ 1 + officer_rate_f * assignment_current_choice,
data = ds)
Residuals:
Min 1Q Median 3Q Max
-3.2668 -0.7113 0.0623 0.9107 2.8582
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.86386 0.14993 25.771 < 2e-16
officer_rate_f4 0.48260 0.19371 2.491 0.01294
officer_rate_f5 0.63055 0.21613 2.918 0.00363
officer_rate_f6 1.04656 0.25795 4.057 5.48e-05
assignment_current_choice -0.43052 0.06608 -6.515 1.33e-10
officer_rate_f4:assignment_current_choice 0.02178 0.08962 0.243 0.80805
officer_rate_f5:assignment_current_choice 0.02543 0.11471 0.222 0.82464
officer_rate_f6:assignment_current_choice -0.21308 0.14358 -1.484 0.13823
Residual standard error: 1.183 on 753 degrees of freedom
(190 observations deleted due to missingness)
Multiple R-squared: 0.2076, Adjusted R-squared: 0.2003
F-statistic: 28.19 on 7 and 753 DF, p-value: < 2.2e-16### favoritism_rank
Call:
lm(formula = favoritism_rank ~ 1 + officer_rate_f * assignment_current_choice,
data = ds)
Residuals:
Min 1Q Median 3Q Max
-2.6992 -0.6334 0.3008 1.3008 2.8981
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.86871 0.18893 20.477 < 2e-16
officer_rate_f4 0.11742 0.23244 0.505 0.614
officer_rate_f5 0.09373 0.25022 0.375 0.708
officer_rate_f6 0.37978 0.28608 1.328 0.185
assignment_current_choice -0.31719 0.07984 -3.973 7.88e-05
officer_rate_f4:assignment_current_choice -0.03550 0.10364 -0.343 0.732
officer_rate_f5:assignment_current_choice -0.05491 0.12738 -0.431 0.667
officer_rate_f6:assignment_current_choice -0.23211 0.14660 -1.583 0.114
Residual standard error: 1.236 on 665 degrees of freedom
(278 observations deleted due to missingness)
Multiple R-squared: 0.1082, Adjusted R-squared: 0.09883
F-statistic: 11.53 on 7 and 665 DF, p-value: 7.273e-145.3 By Rank and Bonus Pay
### satistfaction_rank
Call:
lm(formula = satistfaction_rank ~ 1 + officer_rate_f + bonus_pay,
data = ds)
Residuals:
Min 1Q Median 3Q Max
-3.2614 -0.8433 0.2141 1.0756 2.0756
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.924e+00 1.133e-01 25.818 < 2e-16
officer_rate_f4 2.934e-01 1.267e-01 2.316 0.02081
officer_rate_f5 6.033e-01 1.409e-01 4.282 2.06e-05
officer_rate_f6 8.478e-01 1.621e-01 5.231 2.11e-07
bonus_pay 1.578e-05 5.137e-06 3.072 0.00219
Residual standard error: 1.312 on 865 degrees of freedom
(81 observations deleted due to missingness)
Multiple R-squared: 0.07203, Adjusted R-squared: 0.06774
F-statistic: 16.79 on 4 and 865 DF, p-value: 2.919e-13
Call:
lm(formula = satistfaction_rank ~ 1 + officer_rate_f * bonus_pay,
data = ds)
Residuals:
Min 1Q Median 3Q Max
-3.2319 -0.6738 0.3982 1.1132 2.3262
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.674e+00 1.412e-01 18.934 < 2e-16
officer_rate_f4 9.877e-01 2.499e-01 3.953 8.36e-05
officer_rate_f5 8.154e-01 3.806e-01 2.142 0.03245
officer_rate_f6 1.178e+00 4.843e-01 2.433 0.01516
bonus_pay 3.446e-05 8.141e-06 4.232 2.56e-05
officer_rate_f4:bonus_pay -3.744e-05 1.154e-05 -3.246 0.00122
officer_rate_f5:bonus_pay -1.717e-05 1.566e-05 -1.097 0.27309
officer_rate_f6:bonus_pay -2.221e-05 2.130e-05 -1.042 0.29750
Residual standard error: 1.306 on 862 degrees of freedom
(81 observations deleted due to missingness)
Multiple R-squared: 0.08328, Adjusted R-squared: 0.07584
F-statistic: 11.19 on 7 and 862 DF, p-value: 1.323e-13Analysis of Variance Table
Model 1: satistfaction_rank ~ 1 + officer_rate_f + bonus_pay
Model 2: satistfaction_rank ~ 1 + officer_rate_f * bonus_pay
Res.Df RSS Df Sum of Sq F Pr(>F)
1 865 1488.7
2 862 1470.7 3 18.052 3.5268 0.01461### transparency_rank
Call:
lm(formula = transparency_rank ~ 1 + officer_rate_f * bonus_pay,
data = ds)
Residuals:
Min 1Q Median 3Q Max
-3.1906 -1.1164 0.3004 1.1290 2.5204
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.480e+00 1.433e-01 17.305 < 2e-16
officer_rate_f4 1.008e+00 2.532e-01 3.979 7.49e-05
officer_rate_f5 8.585e-01 3.902e-01 2.200 0.028052
officer_rate_f6 8.102e-01 5.006e-01 1.618 0.105942
bonus_pay 3.184e-05 8.306e-06 3.834 0.000135
officer_rate_f4:bonus_pay -3.155e-05 1.174e-05 -2.688 0.007329
officer_rate_f5:bonus_pay -1.376e-05 1.609e-05 -0.855 0.392553
officer_rate_f6:bonus_pay -2.782e-06 2.213e-05 -0.126 0.899981
Residual standard error: 1.341 on 867 degrees of freedom
(76 observations deleted due to missingness)
Multiple R-squared: 0.08798, Adjusted R-squared: 0.08061
F-statistic: 11.95 on 7 and 867 DF, p-value: 1.334e-14### favoritism_rank
Call:
lm(formula = favoritism_rank ~ 1 + officer_rate_f * bonus_pay,
data = ds)
Residuals:
Min 1Q Median 3Q Max
-2.5064 -1.2309 -0.2692 1.3236 2.1537
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.846e+00 1.679e-01 16.954 <2e-16
officer_rate_f4 6.601e-01 2.737e-01 2.412 0.0161
officer_rate_f5 2.419e-01 4.030e-01 0.600 0.5484
officer_rate_f6 6.359e-01 5.053e-01 1.258 0.2086
bonus_pay 2.114e-05 9.801e-06 2.157 0.0313
officer_rate_f4:bonus_pay -2.880e-05 1.298e-05 -2.219 0.0268
officer_rate_f5:bonus_pay -9.820e-06 1.691e-05 -0.581 0.5615
officer_rate_f6:bonus_pay -2.393e-05 2.245e-05 -1.066 0.2869
Residual standard error: 1.327 on 755 degrees of freedom
(188 observations deleted due to missingness)
Multiple R-squared: 0.01366, Adjusted R-squared: 0.004515
F-statistic: 1.494 on 7 and 755 DF, p-value: 0.16615.4 By Billet Current and Critical War
### satistfaction_rank
Call:
lm(formula = satistfaction_rank ~ 1 + billet_current + critical_war,
data = ds)
Residuals:
Min 1Q Median 3Q Max
-3.0000 -0.8579 0.1421 1.1421 2.5585
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.84828 0.12216 31.502 < 2e-16
billet_currentGME -0.04701 0.13984 -0.336 0.73684
billet_currentNon-Operational/Non-Clinical -0.21070 0.19955 -1.056 0.29133
billet_currentOCONUS MTF -0.44630 0.14528 -3.072 0.00219
billet_currentCONUS Operational -0.74040 0.13873 -5.337 1.21e-07
billet_currentOCONUS Operational -1.40673 0.21686 -6.487 1.48e-10
billet_currentOther 0.14206 0.44292 0.321 0.74849
critical_warLow Deployer 0.00966 0.12369 0.078 0.93777
Residual standard error: 1.315 on 864 degrees of freedom
(79 observations deleted due to missingness)
Multiple R-squared: 0.07332, Adjusted R-squared: 0.06581
F-statistic: 9.766 on 7 and 864 DF, p-value: 9.558e-126 Session Information
For the sake of documentation and reproducibility, the current report was rendered in the following environment. Click the line below to expand.
Environment
Session info -------------------------------------------------------------------------------------- setting value
version R version 3.4.4 (2018-03-15)
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
tz America/Chicago
date 2018-06-30 Packages ------------------------------------------------------------------------------------------ package * version date source
assertthat 0.2.0 2017-04-11 cran (@0.2.0)
backports 1.1.2 2017-12-13 cran (@1.1.2)
base * 3.4.4 2018-04-21 local
bindr 0.1.1 2018-03-13 CRAN (R 3.4.3)
bindrcpp * 0.2.2 2018-03-29 CRAN (R 3.4.3)
colorspace 1.3-2 2016-12-14 CRAN (R 3.4.3)
compiler 3.4.4 2018-04-21 local
corrplot 0.84 2017-10-16 CRAN (R 3.4.3)
datasets * 3.4.4 2018-04-21 local
devtools 1.13.5 2018-02-18 CRAN (R 3.4.3)
digest 0.6.15 2018-01-28 CRAN (R 3.4.3)
dplyr 0.7.5 2018-05-19 CRAN (R 3.4.4)
evaluate 0.10.1 2017-06-24 CRAN (R 3.4.3)
ggplot2 * 2.2.1 2016-12-30 CRAN (R 3.4.4)
glue 1.2.0 2017-10-29 cran (@1.2.0)
graphics * 3.4.4 2018-04-21 local
grDevices * 3.4.4 2018-04-21 local
grid 3.4.4 2018-04-21 local
gtable 0.2.0 2016-02-26 CRAN (R 3.4.3)
highr 0.7 2018-06-09 CRAN (R 3.4.4)
hms 0.4.2.9000 2018-05-26 Github (tidyverse/hms@14e74ab)
htmltools 0.3.6 2017-04-28 CRAN (R 3.4.3)
knitr * 1.20 2018-02-20 CRAN (R 3.4.3)
labeling 0.3 2014-08-23 CRAN (R 3.4.3)
lazyeval 0.2.1 2017-10-29 CRAN (R 3.4.3)
magrittr * 1.5 2014-11-22 cran (@1.5)
memoise 1.1.0 2017-04-21 CRAN (R 3.4.3)
methods * 3.4.4 2018-04-21 local
munsell 0.5.0 2018-06-12 CRAN (R 3.4.4)
pillar 1.2.3 2018-05-25 CRAN (R 3.4.4)
pkgconfig 2.0.1 2017-03-21 cran (@2.0.1)
plyr 1.8.4 2016-06-08 CRAN (R 3.4.3)
purrr 0.2.5 2018-05-29 CRAN (R 3.4.4)
R6 2.2.2 2017-06-17 CRAN (R 3.4.3)
Rcpp 0.12.17 2018-05-18 CRAN (R 3.4.4)
readr 1.2.0 2018-05-26 Github (tidyverse/readr@d6d622b)
rlang 0.2.1 2018-05-30 CRAN (R 3.4.4)
rmarkdown 1.10 2018-06-11 CRAN (R 3.4.4)
rprojroot 1.3-2 2018-01-03 CRAN (R 3.4.3)
scales 0.5.0.9000 2018-03-29 Github (hadley/scales@d767915)
stats * 3.4.4 2018-04-21 local
stringi 1.2.3 2018-06-12 CRAN (R 3.4.4)
stringr 1.3.1 2018-05-10 CRAN (R 3.4.4)
TabularManifest 0.1-16.9003 2018-03-29 Github (Melinae/TabularManifest@c2bdddb)
tibble 1.4.2 2018-01-22 CRAN (R 3.4.3)
tidyr 0.8.1 2018-05-18 CRAN (R 3.4.4)
tidyselect 0.2.4 2018-02-26 CRAN (R 3.4.3)
tools 3.4.4 2018-04-21 local
utils * 3.4.4 2018-04-21 local
withr 2.1.2 2018-03-29 Github (jimhester/withr@79d7b0d)
yaml 2.1.19 2018-05-01 CRAN (R 3.4.4) Report rendered by wibeasley at 2018-06-30, 22:12 -0500 in 61 seconds.